Overview

Dataset statistics

Number of variables33
Number of observations30000
Missing cells0
Missing cells (%)0.0%
Duplicate rows35
Duplicate rows (%)0.1%
Total size in memory7.6 MiB
Average record size in memory264.0 B

Variable types

Numeric20
Categorical13

Alerts

Dataset has 35 (0.1%) duplicate rowsDuplicates
pay_0 is highly overall correlated with pay_2 and 2 other fieldsHigh correlation
pay_2 is highly overall correlated with pay_0 and 7 other fieldsHigh correlation
pay_3 is highly overall correlated with pay_0 and 9 other fieldsHigh correlation
pay_4 is highly overall correlated with pay_0 and 10 other fieldsHigh correlation
pay_5 is highly overall correlated with pay_2 and 8 other fieldsHigh correlation
pay_6 is highly overall correlated with pay_2 and 8 other fieldsHigh correlation
bill_amt1 is highly overall correlated with pay_2 and 8 other fieldsHigh correlation
bill_amt2 is highly overall correlated with pay_2 and 10 other fieldsHigh correlation
bill_amt3 is highly overall correlated with pay_2 and 11 other fieldsHigh correlation
bill_amt4 is highly overall correlated with pay_3 and 13 other fieldsHigh correlation
bill_amt5 is highly overall correlated with pay_3 and 13 other fieldsHigh correlation
bill_amt6 is highly overall correlated with pay_4 and 11 other fieldsHigh correlation
pay_amt1 is highly overall correlated with bill_amt1 and 5 other fieldsHigh correlation
pay_amt2 is highly overall correlated with bill_amt3 and 5 other fieldsHigh correlation
pay_amt3 is highly overall correlated with bill_amt4 and 7 other fieldsHigh correlation
pay_amt4 is highly overall correlated with bill_amt4 and 6 other fieldsHigh correlation
pay_amt5 is highly overall correlated with bill_amt4 and 5 other fieldsHigh correlation
pay_amt6 is highly overall correlated with bill_amt5 and 4 other fieldsHigh correlation
education:1 is highly overall correlated with education:2High correlation
education:2 is highly overall correlated with education:1High correlation
marriage:1 is highly overall correlated with marriage:2High correlation
marriage:2 is highly overall correlated with marriage:1High correlation
education:0 is highly imbalanced (99.4%)Imbalance
education:4 is highly imbalanced (96.2%)Imbalance
education:5 is highly imbalanced (92.4%)Imbalance
education:6 is highly imbalanced (98.2%)Imbalance
marriage:0 is highly imbalanced (98.1%)Imbalance
marriage:3 is highly imbalanced (91.4%)Imbalance
pay_amt2 is highly skewed (γ1 = 30.45381745)Skewed
pay_0 has 14737 (49.1%) zerosZeros
pay_2 has 15730 (52.4%) zerosZeros
pay_3 has 15764 (52.5%) zerosZeros
pay_4 has 16455 (54.9%) zerosZeros
pay_5 has 16947 (56.5%) zerosZeros
pay_6 has 16286 (54.3%) zerosZeros
bill_amt1 has 2008 (6.7%) zerosZeros
bill_amt2 has 2506 (8.4%) zerosZeros
bill_amt3 has 2870 (9.6%) zerosZeros
bill_amt4 has 3195 (10.7%) zerosZeros
bill_amt5 has 3506 (11.7%) zerosZeros
bill_amt6 has 4020 (13.4%) zerosZeros
pay_amt1 has 5249 (17.5%) zerosZeros
pay_amt2 has 5396 (18.0%) zerosZeros
pay_amt3 has 5968 (19.9%) zerosZeros
pay_amt4 has 6408 (21.4%) zerosZeros
pay_amt5 has 6703 (22.3%) zerosZeros
pay_amt6 has 7173 (23.9%) zerosZeros

Reproduction

Analysis started2023-02-21 13:58:53.360197
Analysis finished2023-02-21 14:00:12.351990
Duration1 minute and 18.99 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

limit_bal
Real number (ℝ)

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.32
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-21T15:00:12.566882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.66
Coefficient of variation (CV)0.77468541
Kurtosis0.5362629
Mean167484.32
Median Absolute Deviation (MAD)90000
Skewness0.99286696
Sum5.0245297 × 109
Variance1.6834456 × 1010
MonotonicityNot monotonic
2023-02-21T15:00:12.762967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 3365
 
11.2%
20000 1976
 
6.6%
30000 1610
 
5.4%
80000 1567
 
5.2%
200000 1528
 
5.1%
150000 1110
 
3.7%
100000 1048
 
3.5%
180000 995
 
3.3%
360000 881
 
2.9%
60000 825
 
2.8%
Other values (71) 15095
50.3%
ValueCountFrequency (%)
10000 493
 
1.6%
16000 2
 
< 0.1%
20000 1976
6.6%
30000 1610
5.4%
40000 230
 
0.8%
50000 3365
11.2%
60000 825
 
2.8%
70000 731
 
2.4%
80000 1567
5.2%
90000 651
 
2.2%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
800000 2
 
< 0.1%
780000 2
 
< 0.1%
760000 1
 
< 0.1%
750000 4
< 0.1%
740000 2
 
< 0.1%
730000 2
 
< 0.1%
720000 3
 
< 0.1%
710000 6
< 0.1%
700000 8
< 0.1%

age
Real number (ℝ)

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4855
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-21T15:00:12.957405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2179041
Coefficient of variation (CV)0.25976537
Kurtosis0.044303378
Mean35.4855
Median Absolute Deviation (MAD)6
Skewness0.73224587
Sum1064565
Variance84.969755
MonotonicityNot monotonic
2023-02-21T15:00:13.108239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1605
 
5.3%
27 1477
 
4.9%
28 1409
 
4.7%
30 1395
 
4.7%
26 1256
 
4.2%
31 1217
 
4.1%
25 1186
 
4.0%
34 1162
 
3.9%
32 1158
 
3.9%
33 1146
 
3.8%
Other values (46) 16989
56.6%
ValueCountFrequency (%)
21 67
 
0.2%
22 560
 
1.9%
23 931
3.1%
24 1127
3.8%
25 1186
4.0%
26 1256
4.2%
27 1477
4.9%
28 1409
4.7%
29 1605
5.3%
30 1395
4.7%
ValueCountFrequency (%)
79 1
 
< 0.1%
75 3
 
< 0.1%
74 1
 
< 0.1%
73 4
 
< 0.1%
72 3
 
< 0.1%
71 3
 
< 0.1%
70 10
< 0.1%
69 15
0.1%
68 5
 
< 0.1%
67 16
0.1%

pay_0
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0167
Minimum-2
Maximum8
Zeros14737
Zeros (%)49.1%
Negative8445
Negative (%)28.1%
Memory size234.5 KiB
2023-02-21T15:00:13.258624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1238015
Coefficient of variation (CV)-67.293505
Kurtosis2.720715
Mean-0.0167
Median Absolute Deviation (MAD)1
Skewness0.73197493
Sum-501
Variance1.2629299
MonotonicityNot monotonic
2023-02-21T15:00:13.376696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 14737
49.1%
-1 5686
 
19.0%
1 3688
 
12.3%
-2 2759
 
9.2%
2 2667
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
8 19
 
0.1%
6 11
 
< 0.1%
ValueCountFrequency (%)
-2 2759
 
9.2%
-1 5686
 
19.0%
0 14737
49.1%
1 3688
 
12.3%
2 2667
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
6 11
 
< 0.1%
7 9
 
< 0.1%
ValueCountFrequency (%)
8 19
 
0.1%
7 9
 
< 0.1%
6 11
 
< 0.1%
5 26
 
0.1%
4 76
 
0.3%
3 322
 
1.1%
2 2667
 
8.9%
1 3688
 
12.3%
0 14737
49.1%
-1 5686
 
19.0%

pay_2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.13376667
Minimum-2
Maximum8
Zeros15730
Zeros (%)52.4%
Negative9832
Negative (%)32.8%
Memory size234.5 KiB
2023-02-21T15:00:13.503773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.197186
Coefficient of variation (CV)-8.9498079
Kurtosis1.5704177
Mean-0.13376667
Median Absolute Deviation (MAD)0
Skewness0.79056502
Sum-4013
Variance1.4332543
MonotonicityNot monotonic
2023-02-21T15:00:13.661225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15730
52.4%
-1 6050
 
20.2%
2 3927
 
13.1%
-2 3782
 
12.6%
3 326
 
1.1%
4 99
 
0.3%
1 28
 
0.1%
5 25
 
0.1%
7 20
 
0.1%
6 12
 
< 0.1%
ValueCountFrequency (%)
-2 3782
 
12.6%
-1 6050
 
20.2%
0 15730
52.4%
1 28
 
0.1%
2 3927
 
13.1%
3 326
 
1.1%
4 99
 
0.3%
5 25
 
0.1%
6 12
 
< 0.1%
7 20
 
0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 20
 
0.1%
6 12
 
< 0.1%
5 25
 
0.1%
4 99
 
0.3%
3 326
 
1.1%
2 3927
 
13.1%
1 28
 
0.1%
0 15730
52.4%
-1 6050
 
20.2%

pay_3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1662
Minimum-2
Maximum8
Zeros15764
Zeros (%)52.5%
Negative10023
Negative (%)33.4%
Memory size234.5 KiB
2023-02-21T15:00:13.830270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1968676
Coefficient of variation (CV)-7.2013692
Kurtosis2.0844359
Mean-0.1662
Median Absolute Deviation (MAD)0
Skewness0.84068183
Sum-4986
Variance1.432492
MonotonicityNot monotonic
2023-02-21T15:00:13.954142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15764
52.5%
-1 5938
 
19.8%
-2 4085
 
13.6%
2 3819
 
12.7%
3 240
 
0.8%
4 76
 
0.3%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
1 4
 
< 0.1%
ValueCountFrequency (%)
-2 4085
 
13.6%
-1 5938
 
19.8%
0 15764
52.5%
1 4
 
< 0.1%
2 3819
 
12.7%
3 240
 
0.8%
4 76
 
0.3%
5 21
 
0.1%
6 23
 
0.1%
7 27
 
0.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
4 76
 
0.3%
3 240
 
0.8%
2 3819
 
12.7%
1 4
 
< 0.1%
0 15764
52.5%
-1 5938
 
19.8%

pay_4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.22066667
Minimum-2
Maximum8
Zeros16455
Zeros (%)54.9%
Negative10035
Negative (%)33.5%
Memory size234.5 KiB
2023-02-21T15:00:14.124188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1691386
Coefficient of variation (CV)-5.2982113
Kurtosis3.4969835
Mean-0.22066667
Median Absolute Deviation (MAD)0
Skewness0.99962941
Sum-6620
Variance1.3668851
MonotonicityNot monotonic
2023-02-21T15:00:14.278835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16455
54.9%
-1 5687
 
19.0%
-2 4348
 
14.5%
2 3159
 
10.5%
3 180
 
0.6%
4 69
 
0.2%
7 58
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
-2 4348
 
14.5%
-1 5687
 
19.0%
0 16455
54.9%
1 2
 
< 0.1%
2 3159
 
10.5%
3 180
 
0.6%
4 69
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
7 58
 
0.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 58
 
0.2%
6 5
 
< 0.1%
5 35
 
0.1%
4 69
 
0.2%
3 180
 
0.6%
2 3159
 
10.5%
1 2
 
< 0.1%
0 16455
54.9%
-1 5687
 
19.0%

pay_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2662
Minimum-2
Maximum8
Zeros16947
Zeros (%)56.5%
Negative10085
Negative (%)33.6%
Memory size234.5 KiB
2023-02-21T15:00:14.471524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1331874
Coefficient of variation (CV)-4.2569024
Kurtosis3.9897481
Mean-0.2662
Median Absolute Deviation (MAD)0
Skewness1.008197
Sum-7986
Variance1.2841137
MonotonicityNot monotonic
2023-02-21T15:00:14.642264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16947
56.5%
-1 5539
 
18.5%
-2 4546
 
15.2%
2 2626
 
8.8%
3 178
 
0.6%
4 84
 
0.3%
7 58
 
0.2%
5 17
 
0.1%
6 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
-2 4546
 
15.2%
-1 5539
 
18.5%
0 16947
56.5%
2 2626
 
8.8%
3 178
 
0.6%
4 84
 
0.3%
5 17
 
0.1%
6 4
 
< 0.1%
7 58
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 58
 
0.2%
6 4
 
< 0.1%
5 17
 
0.1%
4 84
 
0.3%
3 178
 
0.6%
2 2626
 
8.8%
0 16947
56.5%
-1 5539
 
18.5%
-2 4546
 
15.2%

pay_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2911
Minimum-2
Maximum8
Zeros16286
Zeros (%)54.3%
Negative10635
Negative (%)35.4%
Memory size234.5 KiB
2023-02-21T15:00:14.822830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1499876
Coefficient of variation (CV)-3.95049
Kurtosis3.4265341
Mean-0.2911
Median Absolute Deviation (MAD)0
Skewness0.94802939
Sum-8733
Variance1.3224715
MonotonicityNot monotonic
2023-02-21T15:00:14.973894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16286
54.3%
-1 5740
 
19.1%
-2 4895
 
16.3%
2 2766
 
9.2%
3 184
 
0.6%
4 49
 
0.2%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
-2 4895
 
16.3%
-1 5740
 
19.1%
0 16286
54.3%
2 2766
 
9.2%
3 184
 
0.6%
4 49
 
0.2%
5 13
 
< 0.1%
6 19
 
0.1%
7 46
 
0.2%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
4 49
 
0.2%
3 184
 
0.6%
2 2766
 
9.2%
0 16286
54.3%
-1 5740
 
19.1%
-2 4895
 
16.3%

bill_amt1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22723
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.331
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Negative590
Negative (%)2.0%
Memory size234.5 KiB
2023-02-21T15:00:15.177311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.861
Coefficient of variation (CV)1.4375453
Kurtosis9.8062893
Mean51223.331
Median Absolute Deviation (MAD)21800.5
Skewness2.663861
Sum1.5366999 × 109
Variance5.42224 × 109
MonotonicityNot monotonic
2023-02-21T15:00:15.366569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2008
 
6.7%
390 244
 
0.8%
780 76
 
0.3%
326 72
 
0.2%
316 63
 
0.2%
2500 59
 
0.2%
396 49
 
0.2%
2400 39
 
0.1%
416 29
 
0.1%
500 25
 
0.1%
Other values (22713) 27336
91.1%
ValueCountFrequency (%)
-165580 1
< 0.1%
-154973 1
< 0.1%
-15308 1
< 0.1%
-14386 1
< 0.1%
-11545 1
< 0.1%
-10682 1
< 0.1%
-9802 1
< 0.1%
-9095 1
< 0.1%
-8187 1
< 0.1%
-7438 1
< 0.1%
ValueCountFrequency (%)
964511 1
< 0.1%
746814 1
< 0.1%
653062 1
< 0.1%
630458 1
< 0.1%
626648 1
< 0.1%
621749 1
< 0.1%
613860 1
< 0.1%
610723 1
< 0.1%
608594 1
< 0.1%
604019 1
< 0.1%

bill_amt2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22346
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.075
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Negative669
Negative (%)2.2%
Memory size234.5 KiB
2023-02-21T15:00:15.702303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.769
Coefficient of variation (CV)1.4472368
Kurtosis10.302946
Mean49179.075
Median Absolute Deviation (MAD)20810
Skewness2.7052209
Sum1.4753723 × 109
Variance5.0657054 × 109
MonotonicityNot monotonic
2023-02-21T15:00:15.970862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2506
 
8.4%
390 231
 
0.8%
780 75
 
0.2%
326 75
 
0.2%
316 72
 
0.2%
2500 51
 
0.2%
396 51
 
0.2%
2400 42
 
0.1%
-200 29
 
0.1%
416 28
 
0.1%
Other values (22336) 26840
89.5%
ValueCountFrequency (%)
-69777 1
< 0.1%
-67526 1
< 0.1%
-33350 1
< 0.1%
-30000 1
< 0.1%
-26214 1
< 0.1%
-24704 1
< 0.1%
-24702 1
< 0.1%
-22960 1
< 0.1%
-18618 1
< 0.1%
-18088 1
< 0.1%
ValueCountFrequency (%)
983931 1
< 0.1%
743970 1
< 0.1%
671563 1
< 0.1%
646770 1
< 0.1%
624475 1
< 0.1%
605943 1
< 0.1%
597793 1
< 0.1%
586825 1
< 0.1%
581775 1
< 0.1%
577681 1
< 0.1%

bill_amt3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22026
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.155
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2023-02-21T15:00:16.233839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.387
Coefficient of variation (CV)1.475106
Kurtosis19.783255
Mean47013.155
Median Absolute Deviation (MAD)19708.5
Skewness3.08783
Sum1.4103946 × 109
Variance4.8093375 × 109
MonotonicityNot monotonic
2023-02-21T15:00:16.465633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2870
 
9.6%
390 275
 
0.9%
780 74
 
0.2%
326 63
 
0.2%
316 62
 
0.2%
396 48
 
0.2%
2500 40
 
0.1%
2400 39
 
0.1%
416 29
 
0.1%
200 27
 
0.1%
Other values (22016) 26473
88.2%
ValueCountFrequency (%)
-157264 1
< 0.1%
-61506 1
< 0.1%
-46127 1
< 0.1%
-34041 1
< 0.1%
-25443 1
< 0.1%
-24702 1
< 0.1%
-20320 1
< 0.1%
-17706 1
< 0.1%
-15910 1
< 0.1%
-15641 1
< 0.1%
ValueCountFrequency (%)
1664089 1
< 0.1%
855086 1
< 0.1%
693131 1
< 0.1%
689643 1
< 0.1%
689627 1
< 0.1%
632041 1
< 0.1%
597415 1
< 0.1%
578971 1
< 0.1%
577957 1
< 0.1%
577015 1
< 0.1%

bill_amt4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21548
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.949
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Negative675
Negative (%)2.2%
Memory size234.5 KiB
2023-02-21T15:00:16.731620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.856
Coefficient of variation (CV)1.4870197
Kurtosis11.309325
Mean43262.949
Median Absolute Deviation (MAD)18656
Skewness2.8219653
Sum1.2978885 × 109
Variance4.1387164 × 109
MonotonicityNot monotonic
2023-02-21T15:00:16.951675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3195
 
10.7%
390 246
 
0.8%
780 101
 
0.3%
316 68
 
0.2%
326 62
 
0.2%
396 44
 
0.1%
2400 39
 
0.1%
150 39
 
0.1%
2500 34
 
0.1%
416 33
 
0.1%
Other values (21538) 26139
87.1%
ValueCountFrequency (%)
-170000 1
< 0.1%
-81334 1
< 0.1%
-65167 1
< 0.1%
-50616 1
< 0.1%
-46627 1
< 0.1%
-34503 1
< 0.1%
-27490 1
< 0.1%
-24303 1
< 0.1%
-22108 1
< 0.1%
-20320 1
< 0.1%
ValueCountFrequency (%)
891586 1
< 0.1%
706864 1
< 0.1%
628699 1
< 0.1%
616836 1
< 0.1%
572805 1
< 0.1%
569034 1
< 0.1%
565669 1
< 0.1%
563543 1
< 0.1%
548020 1
< 0.1%
542653 1
< 0.1%

bill_amt5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21010
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.401
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2023-02-21T15:00:17.126668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.156
Coefficient of variation (CV)1.5081876
Kurtosis12.305881
Mean40311.401
Median Absolute Deviation (MAD)17688.5
Skewness2.8763799
Sum1.209342 × 109
Variance3.6962941 × 109
MonotonicityNot monotonic
2023-02-21T15:00:17.285460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3506
 
11.7%
390 235
 
0.8%
780 94
 
0.3%
316 79
 
0.3%
326 62
 
0.2%
150 58
 
0.2%
396 47
 
0.2%
2400 39
 
0.1%
2500 37
 
0.1%
416 36
 
0.1%
Other values (21000) 25807
86.0%
ValueCountFrequency (%)
-81334 1
< 0.1%
-61372 1
< 0.1%
-53007 1
< 0.1%
-46627 1
< 0.1%
-37594 1
< 0.1%
-36156 1
< 0.1%
-30481 1
< 0.1%
-28335 1
< 0.1%
-23003 1
< 0.1%
-20753 1
< 0.1%
ValueCountFrequency (%)
927171 1
< 0.1%
823540 1
< 0.1%
587067 1
< 0.1%
551702 1
< 0.1%
547880 1
< 0.1%
530672 1
< 0.1%
524315 1
< 0.1%
516139 1
< 0.1%
514114 1
< 0.1%
508213 1
< 0.1%

bill_amt6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20604
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.76
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Negative688
Negative (%)2.3%
Memory size234.5 KiB
2023-02-21T15:00:17.436550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.108
Coefficient of variation (CV)1.5320661
Kurtosis12.270705
Mean38871.76
Median Absolute Deviation (MAD)16755
Skewness2.8466446
Sum1.1661528 × 109
Variance3.5466917 × 109
MonotonicityNot monotonic
2023-02-21T15:00:17.614897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4020
 
13.4%
390 207
 
0.7%
780 86
 
0.3%
150 78
 
0.3%
316 77
 
0.3%
326 56
 
0.2%
396 45
 
0.1%
416 36
 
0.1%
-18 33
 
0.1%
2400 32
 
0.1%
Other values (20594) 25330
84.4%
ValueCountFrequency (%)
-339603 1
< 0.1%
-209051 1
< 0.1%
-150953 1
< 0.1%
-94625 1
< 0.1%
-73895 1
< 0.1%
-57060 1
< 0.1%
-51443 1
< 0.1%
-51183 1
< 0.1%
-46627 1
< 0.1%
-45734 1
< 0.1%
ValueCountFrequency (%)
961664 1
< 0.1%
699944 1
< 0.1%
568638 1
< 0.1%
527711 1
< 0.1%
527566 1
< 0.1%
514975 1
< 0.1%
513798 1
< 0.1%
511905 1
< 0.1%
501370 1
< 0.1%
499100 1
< 0.1%

pay_amt1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-21T15:00:17.820163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28
Coefficient of variation (CV)2.9245246
Kurtosis415.25474
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.668364
Sum1.6990742 × 108
Variance2.7434226 × 108
MonotonicityNot monotonic
2023-02-21T15:00:18.053222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5249
 
17.5%
2000 1363
 
4.5%
3000 891
 
3.0%
5000 698
 
2.3%
1500 507
 
1.7%
4000 426
 
1.4%
10000 401
 
1.3%
1000 365
 
1.2%
2500 298
 
1.0%
6000 294
 
1.0%
Other values (7933) 19508
65.0%
ValueCountFrequency (%)
0 5249
17.5%
1 9
 
< 0.1%
2 14
 
< 0.1%
3 15
 
0.1%
4 18
 
0.1%
5 12
 
< 0.1%
6 15
 
0.1%
7 9
 
< 0.1%
8 8
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
873552 1
< 0.1%
505000 1
< 0.1%
493358 1
< 0.1%
423903 1
< 0.1%
405016 1
< 0.1%
368199 1
< 0.1%
323014 1
< 0.1%
304815 1
< 0.1%
302000 1
< 0.1%
300039 1
< 0.1%

pay_amt2
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7899
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-21T15:00:18.244002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.87
Coefficient of variation (CV)3.8912741
Kurtosis1641.6319
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.453817
Sum1.776349 × 108
Variance5.3088171 × 108
MonotonicityNot monotonic
2023-02-21T15:00:18.378084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5396
 
18.0%
2000 1290
 
4.3%
3000 857
 
2.9%
5000 717
 
2.4%
1000 594
 
2.0%
1500 521
 
1.7%
4000 410
 
1.4%
10000 318
 
1.1%
6000 283
 
0.9%
2500 251
 
0.8%
Other values (7889) 19363
64.5%
ValueCountFrequency (%)
0 5396
18.0%
1 15
 
0.1%
2 20
 
0.1%
3 18
 
0.1%
4 11
 
< 0.1%
5 25
 
0.1%
6 8
 
< 0.1%
7 12
 
< 0.1%
8 9
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
1684259 1
< 0.1%
1227082 1
< 0.1%
1215471 1
< 0.1%
1024516 1
< 0.1%
580464 1
< 0.1%
415552 1
< 0.1%
401003 1
< 0.1%
388126 1
< 0.1%
385228 1
< 0.1%
384986 1
< 0.1%

pay_amt3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-21T15:00:18.550600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.961
Coefficient of variation (CV)3.3693139
Kurtosis564.31123
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.216635
Sum1.5677044 × 108
Variance3.1000509 × 108
MonotonicityNot monotonic
2023-02-21T15:00:18.692777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5968
 
19.9%
2000 1285
 
4.3%
1000 1103
 
3.7%
3000 870
 
2.9%
5000 721
 
2.4%
1500 490
 
1.6%
4000 381
 
1.3%
10000 312
 
1.0%
1200 243
 
0.8%
6000 241
 
0.8%
Other values (7508) 18386
61.3%
ValueCountFrequency (%)
0 5968
19.9%
1 13
 
< 0.1%
2 19
 
0.1%
3 14
 
< 0.1%
4 15
 
0.1%
5 18
 
0.1%
6 14
 
< 0.1%
7 18
 
0.1%
8 10
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
896040 1
< 0.1%
889043 1
< 0.1%
508229 1
< 0.1%
417588 1
< 0.1%
400972 1
< 0.1%
397092 1
< 0.1%
380478 1
< 0.1%
371718 1
< 0.1%
349395 1
< 0.1%
344261 1
< 0.1%

pay_amt4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6937
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.0769
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-21T15:00:18.866754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.16
Coefficient of variation (CV)3.246148
Kurtosis277.33377
Mean4826.0769
Median Absolute Deviation (MAD)1500
Skewness12.904985
Sum1.4478231 × 108
Variance2.4542856 × 108
MonotonicityNot monotonic
2023-02-21T15:00:19.017676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6408
 
21.4%
1000 1394
 
4.6%
2000 1214
 
4.0%
3000 887
 
3.0%
5000 810
 
2.7%
1500 441
 
1.5%
4000 402
 
1.3%
10000 341
 
1.1%
2500 259
 
0.9%
500 258
 
0.9%
Other values (6927) 17586
58.6%
ValueCountFrequency (%)
0 6408
21.4%
1 22
 
0.1%
2 22
 
0.1%
3 13
 
< 0.1%
4 20
 
0.1%
5 12
 
< 0.1%
6 16
 
0.1%
7 11
 
< 0.1%
8 7
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
621000 1
< 0.1%
528897 1
< 0.1%
497000 1
< 0.1%
432130 1
< 0.1%
400046 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
320008 1
< 0.1%
313094 1
< 0.1%
292962 1
< 0.1%

pay_amt5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.3876
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-21T15:00:19.180046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.306
Coefficient of variation (CV)3.1833865
Kurtosis180.06394
Mean4799.3876
Median Absolute Deviation (MAD)1500
Skewness11.127417
Sum1.4398163 × 108
Variance2.3342662 × 108
MonotonicityNot monotonic
2023-02-21T15:00:19.303073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6703
 
22.3%
1000 1340
 
4.5%
2000 1323
 
4.4%
3000 947
 
3.2%
5000 814
 
2.7%
1500 426
 
1.4%
4000 401
 
1.3%
10000 343
 
1.1%
500 250
 
0.8%
6000 247
 
0.8%
Other values (6887) 17206
57.4%
ValueCountFrequency (%)
0 6703
22.3%
1 21
 
0.1%
2 13
 
< 0.1%
3 13
 
< 0.1%
4 12
 
< 0.1%
5 9
 
< 0.1%
6 7
 
< 0.1%
7 9
 
< 0.1%
8 6
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
426529 1
< 0.1%
417990 1
< 0.1%
388071 1
< 0.1%
379267 1
< 0.1%
332000 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
326889 1
< 0.1%
317077 1
< 0.1%
310135 1
< 0.1%

pay_amt6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6939
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.5026
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2023-02-21T15:00:19.455650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.466
Coefficient of variation (CV)3.4085815
Kurtosis167.16143
Mean5215.5026
Median Absolute Deviation (MAD)1500
Skewness10.640727
Sum1.5646508 × 108
Variance3.1603829 × 108
MonotonicityNot monotonic
2023-02-21T15:00:19.595849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7173
23.9%
1000 1299
 
4.3%
2000 1295
 
4.3%
3000 914
 
3.0%
5000 808
 
2.7%
1500 439
 
1.5%
4000 411
 
1.4%
10000 356
 
1.2%
500 247
 
0.8%
6000 220
 
0.7%
Other values (6929) 16838
56.1%
ValueCountFrequency (%)
0 7173
23.9%
1 20
 
0.1%
2 9
 
< 0.1%
3 14
 
< 0.1%
4 12
 
< 0.1%
5 7
 
< 0.1%
6 6
 
< 0.1%
7 5
 
< 0.1%
8 6
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
528666 1
< 0.1%
527143 1
< 0.1%
443001 1
< 0.1%
422000 1
< 0.1%
403500 1
< 0.1%
377000 1
< 0.1%
372495 1
< 0.1%
351282 1
< 0.1%
345293 1
< 0.1%
308000 1
< 0.1%

sex:2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1.0
18112 
0.0
11888 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 18112
60.4%
0.0 11888
39.6%

Length

2023-02-21T15:00:19.745653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:19.862513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 18112
60.4%
0.0 11888
39.6%

Most occurring characters

ValueCountFrequency (%)
0 41888
46.5%
. 30000
33.3%
1 18112
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41888
69.8%
1 18112
30.2%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41888
46.5%
. 30000
33.3%
1 18112
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41888
46.5%
. 30000
33.3%
1 18112
20.1%

education:0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
29986 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 29986
> 99.9%
1.0 14
 
< 0.1%

Length

2023-02-21T15:00:19.962064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:20.079073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29986
> 99.9%
1.0 14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 59986
66.7%
. 30000
33.3%
1 14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59986
> 99.9%
1 14
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59986
66.7%
. 30000
33.3%
1 14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59986
66.7%
. 30000
33.3%
1 14
 
< 0.1%

education:1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
19415 
1.0
10585 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19415
64.7%
1.0 10585
35.3%

Length

2023-02-21T15:00:20.184743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:20.317645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19415
64.7%
1.0 10585
35.3%

Most occurring characters

ValueCountFrequency (%)
0 49415
54.9%
. 30000
33.3%
1 10585
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49415
82.4%
1 10585
 
17.6%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49415
54.9%
. 30000
33.3%
1 10585
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49415
54.9%
. 30000
33.3%
1 10585
 
11.8%

education:2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
15970 
1.0
14030 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 15970
53.2%
1.0 14030
46.8%

Length

2023-02-21T15:00:20.416852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:20.531566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15970
53.2%
1.0 14030
46.8%

Most occurring characters

ValueCountFrequency (%)
0 45970
51.1%
. 30000
33.3%
1 14030
 
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45970
76.6%
1 14030
 
23.4%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45970
51.1%
. 30000
33.3%
1 14030
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45970
51.1%
. 30000
33.3%
1 14030
 
15.6%

education:3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
25083 
1.0
4917 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 25083
83.6%
1.0 4917
 
16.4%

Length

2023-02-21T15:00:20.632240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:20.753114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 25083
83.6%
1.0 4917
 
16.4%

Most occurring characters

ValueCountFrequency (%)
0 55083
61.2%
. 30000
33.3%
1 4917
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55083
91.8%
1 4917
 
8.2%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55083
61.2%
. 30000
33.3%
1 4917
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55083
61.2%
. 30000
33.3%
1 4917
 
5.5%

education:4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
29877 
1.0
 
123

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 29877
99.6%
1.0 123
 
0.4%

Length

2023-02-21T15:00:20.861244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:20.975411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29877
99.6%
1.0 123
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 59877
66.5%
. 30000
33.3%
1 123
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59877
99.8%
1 123
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59877
66.5%
. 30000
33.3%
1 123
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59877
66.5%
. 30000
33.3%
1 123
 
0.1%

education:5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
29720 
1.0
 
280

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 29720
99.1%
1.0 280
 
0.9%

Length

2023-02-21T15:00:21.073359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:21.192111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29720
99.1%
1.0 280
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 59720
66.4%
. 30000
33.3%
1 280
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59720
99.5%
1 280
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59720
66.4%
. 30000
33.3%
1 280
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59720
66.4%
. 30000
33.3%
1 280
 
0.3%

education:6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
29949 
1.0
 
51

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 29949
99.8%
1.0 51
 
0.2%

Length

2023-02-21T15:00:21.296476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:22.273072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29949
99.8%
1.0 51
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 59949
66.6%
. 30000
33.3%
1 51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59949
99.9%
1 51
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59949
66.6%
. 30000
33.3%
1 51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59949
66.6%
. 30000
33.3%
1 51
 
0.1%

marriage:0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
29946 
1.0
 
54

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 29946
99.8%
1.0 54
 
0.2%

Length

2023-02-21T15:00:22.368116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:22.495996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29946
99.8%
1.0 54
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 59946
66.6%
. 30000
33.3%
1 54
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59946
99.9%
1 54
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59946
66.6%
. 30000
33.3%
1 54
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59946
66.6%
. 30000
33.3%
1 54
 
0.1%

marriage:1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
16341 
1.0
13659 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 16341
54.5%
1.0 13659
45.5%

Length

2023-02-21T15:00:22.594422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:22.705238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16341
54.5%
1.0 13659
45.5%

Most occurring characters

ValueCountFrequency (%)
0 46341
51.5%
. 30000
33.3%
1 13659
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46341
77.2%
1 13659
 
22.8%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46341
51.5%
. 30000
33.3%
1 13659
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46341
51.5%
. 30000
33.3%
1 13659
 
15.2%

marriage:2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1.0
15964 
0.0
14036 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 15964
53.2%
0.0 14036
46.8%

Length

2023-02-21T15:00:22.796831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:22.903505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15964
53.2%
0.0 14036
46.8%

Most occurring characters

ValueCountFrequency (%)
0 44036
48.9%
. 30000
33.3%
1 15964
 
17.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44036
73.4%
1 15964
 
26.6%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44036
48.9%
. 30000
33.3%
1 15964
 
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44036
48.9%
. 30000
33.3%
1 15964
 
17.7%

marriage:3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0.0
29677 
1.0
 
323

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 29677
98.9%
1.0 323
 
1.1%

Length

2023-02-21T15:00:23.005094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:23.122837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 29677
98.9%
1.0 323
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 59677
66.3%
. 30000
33.3%
1 323
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
66.7%
Other Punctuation 30000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59677
99.5%
1 323
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59677
66.3%
. 30000
33.3%
1 323
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59677
66.3%
. 30000
33.3%
1 323
 
0.4%

label
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
23364 
1
6636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Length

2023-02-21T15:00:23.212472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-21T15:00:23.330660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Interactions

2023-02-21T15:00:06.919727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:03.033196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:06.806100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:10.507633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:13.704271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:17.222316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:20.224050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:23.348401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:26.632397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:29.634633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:32.649683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:36.496616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:39.845007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:43.588668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:46.507300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:50.430391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:53.367858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:56.886077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:59.865605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:03.321179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:07.065645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:03.285487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:07.038653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:10.670496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:13.880278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:17.387515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:20.387279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:23.532621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:26.816625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:29.788929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:32.784861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:36.675606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:40.018023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:43.755192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:46.650941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:50.600037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:53.525057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:57.053362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:00.013521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:03.485849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:07.210702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:03.457733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:07.255202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:10.820924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:14.060266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:17.528575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:20.537897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:23.697203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:26.968198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:29.947488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:32.927907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:36.840948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:40.301164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:43.917089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:46.783157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:50.734798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:53.669723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:57.234852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:00.152608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:03.648540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:07.343161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:03.789772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:07.433134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:10.973516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:14.201625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:17.650531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:20.715718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:23.842201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:27.108067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:30.105709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:33.057188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:36.986628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:40.513381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:44.063099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:46.906755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:50.860963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:53.808993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:57.368024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:00.309254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:03.811198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:07.474366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:03.952558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:07.583158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:11.135385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:14.340807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:17.792084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:20.860608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:24.026698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:27.249199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:30.244090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:33.200634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:37.136760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:40.678522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:44.212145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:47.048458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:50.998323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:53.973005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:57.497931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:00.452733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:03.984819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:07.614237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:04.137186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:07.737434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:11.284939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:14.494821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:17.964153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:20.995339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:24.150750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:27.402701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:30.380345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:33.372122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:37.302096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:40.833275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:44.355451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:47.210349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:51.155002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:54.125029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:57.630701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:00.592864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:04.130791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:07.768989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:04.292285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:07.891722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:11.416100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:14.658228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:18.095093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:21.131212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:24.269034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:27.545372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:30.516646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:33.554951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:37.461142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:41.024271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:44.500357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:47.368556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:51.283006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:54.290948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:57.790488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:00.738674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:04.853134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:07.917746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:04.459238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:08.056611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:11.547156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:14.792252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:18.216575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:21.280675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:24.390614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:27.665608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:30.667007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:33.722116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:37.604531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:41.238111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:44.622918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:47.511758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:51.431486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:54.470643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:57.909531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:00.888417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:05.002755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:08.062545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:04.625462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:08.218014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:11.678498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:14.953371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:18.342420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:21.456052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:24.520959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:27.786981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:30.810044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:33.865443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:37.763465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:41.393834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:44.756126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:47.661085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:51.556265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:54.639562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:58.031764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:01.056461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:05.136684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-21T14:59:21.607545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-21T14:59:27.946164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:30.962363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:34.068994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-21T14:59:43.427149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:46.374867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:50.249732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:53.209939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:56.703694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T14:59:59.727433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:03.121225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-21T15:00:06.792299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-02-21T15:00:23.446166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
limit_balagepay_0pay_2pay_3pay_4pay_5pay_6bill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6sex:2education:0education:1education:2education:3education:4education:5education:6marriage:0marriage:1marriage:2marriage:3label
limit_bal1.0000.186-0.296-0.343-0.332-0.309-0.285-0.2640.0540.0490.0610.0730.0810.0880.2720.2780.2840.2830.2940.3170.0730.0070.2610.1440.1510.0430.0140.0000.0080.1000.0870.0580.157
age0.1861.000-0.064-0.083-0.083-0.080-0.083-0.0760.0010.0020.002-0.003-0.0000.0000.0340.0440.0330.0410.0380.0390.0910.0000.1620.1110.2460.0000.0000.0470.0000.4780.4930.0880.048
pay_0-0.296-0.0641.0000.6270.5480.5160.4860.4640.3150.3300.3140.3070.2990.289-0.098-0.064-0.054-0.034-0.026-0.0450.0590.0000.1920.1400.0640.0170.0280.0050.0230.0540.0500.0220.422
pay_2-0.343-0.0830.6271.0000.7990.7130.6740.6350.5710.5510.5190.4980.4780.4590.0200.0840.0870.0950.0990.0820.0710.0060.2070.1540.0670.0290.0310.0000.0000.0570.0540.0170.340
pay_3-0.332-0.0830.5480.7991.0000.8010.7180.6710.5240.5890.5570.5310.5070.4850.2160.0370.1030.1190.1240.0980.0670.0120.2000.1490.0660.0270.0320.0010.0100.0530.0490.0170.294
pay_4-0.309-0.0800.5160.7130.8011.0000.8220.7320.5120.5580.6190.5930.5610.5340.1850.2460.0690.1440.1620.1430.0630.0030.1830.1360.0610.0280.0320.0000.0050.0590.0550.0220.278
pay_5-0.285-0.0830.4860.6740.7180.8221.0000.8210.4990.5380.5870.6500.6180.5790.1750.2220.2600.1070.1850.1720.0560.0130.1670.1250.0530.0230.0320.0000.0000.0550.0510.0210.269
pay_6-0.264-0.0760.4640.6350.6710.7320.8211.0000.4880.5240.5610.6060.6680.6300.1780.2000.2380.2840.1410.1980.0470.0200.1610.1220.0510.0170.0260.0000.0000.0500.0460.0190.249
bill_amt10.0540.0010.3150.5710.5240.5120.4990.4881.0000.9110.8580.8070.7690.7340.5020.4720.4410.4420.4250.4100.0260.0000.0500.0380.0330.0000.0500.0100.0080.0310.0260.0000.031
bill_amt20.0490.0020.3300.5510.5890.5580.5380.5240.9111.0000.9080.8480.8030.7650.6360.4980.4680.4610.4490.4290.0330.0000.0910.0690.0350.0160.0370.0130.0000.0240.0190.0050.031
bill_amt30.0610.0020.3140.5190.5570.6190.5870.5610.8580.9081.0000.9040.8490.8040.5500.6380.4920.4890.4770.4580.0180.0000.0980.0720.0380.0000.0250.0060.0050.0230.0200.0000.000
bill_amt40.073-0.0030.3070.4980.5310.5930.6500.6060.8070.8480.9041.0000.9030.8480.5120.5550.6340.5070.5040.4810.0260.0000.0520.0410.0330.0000.0220.0000.0000.0240.0190.0120.019
bill_amt50.081-0.0000.2990.4780.5070.5610.6180.6680.7690.8030.8490.9031.0000.9020.4830.5150.5490.6470.5250.5090.0210.0000.0950.0730.0370.0060.0190.0000.0020.0260.0220.0090.017
bill_amt60.0880.0000.2890.4590.4850.5340.5790.6300.7340.7650.8040.8480.9021.0000.4560.4870.5190.5700.6660.5290.0260.0000.0330.0320.0380.0000.0000.0000.0000.0240.0190.0180.022
pay_amt10.2720.034-0.0980.0200.2160.1850.1750.1780.5020.6360.5500.5120.4830.4561.0000.5120.5190.4860.4680.4550.0000.0000.0060.0000.0000.0000.0000.0000.0140.0000.0000.0540.027
pay_amt20.2780.044-0.0640.0840.0370.2460.2220.2000.4720.4980.6380.5550.5150.4870.5121.0000.5160.5200.4970.4910.0000.0000.0130.0000.0070.0000.0000.0000.0000.0090.0050.0360.013
pay_amt30.2840.033-0.0540.0870.1030.0690.2600.2380.4410.4680.4920.6340.5490.5190.5190.5161.0000.5160.5340.5050.0120.0000.0310.0180.0150.0100.0110.0000.0000.0000.0000.0380.024
pay_amt40.2830.041-0.0340.0950.1190.1440.1070.2840.4420.4610.4890.5070.6470.5700.4860.5200.5161.0000.5340.5470.0000.0000.0150.0000.0000.0000.0000.0000.0000.0070.0030.0540.022
pay_amt50.2940.038-0.0260.0990.1240.1620.1850.1410.4250.4490.4770.5040.5250.6660.4680.4970.5340.5341.0000.5490.0140.0000.0300.0230.0120.0000.0000.0290.0000.0050.0090.0150.035
pay_amt60.3170.039-0.0450.0820.0980.1430.1720.1980.4100.4290.4580.4810.5090.5290.4550.4910.5050.5470.5491.0000.0120.0000.0310.0160.0190.0000.0350.0410.0000.0080.0090.0000.028
sex:20.0730.0910.0590.0710.0670.0630.0560.0470.0260.0330.0180.0260.0210.0260.0000.0000.0120.0000.0140.0121.0000.0020.0220.0250.0050.0030.0090.0040.0090.0300.0300.0000.039
education:00.0070.0000.0000.0060.0120.0030.0130.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0021.0000.0130.0180.0050.0000.0000.0000.0000.0010.0030.0000.008
education:10.2610.1620.1920.2070.2000.1830.1670.1610.0500.0910.0980.0520.0950.0330.0060.0130.0310.0150.0300.0310.0220.0131.0000.6920.3270.0460.0710.0290.0230.1540.1640.0430.051
education:20.1440.1110.1400.1540.1490.1360.1250.1220.0380.0690.0720.0410.0730.0320.0000.0000.0180.0000.0230.0160.0250.0180.6921.0000.4150.0590.0900.0370.0290.0610.0590.0040.036
education:30.1510.2460.0640.0670.0660.0610.0530.0510.0330.0350.0380.0330.0370.0380.0000.0070.0150.0000.0120.0190.0050.0050.3270.4151.0000.0270.0420.0160.0730.1120.1270.0430.032
education:40.0430.0000.0170.0290.0270.0280.0230.0170.0000.0160.0000.0000.0060.0000.0000.0000.0100.0000.0000.0000.0030.0000.0460.0590.0271.0000.0000.0000.0000.0000.0000.0010.024
education:50.0140.0000.0280.0310.0320.0320.0320.0260.0500.0370.0250.0220.0190.0000.0000.0000.0110.0000.0000.0350.0090.0000.0710.0900.0420.0001.0000.0000.0000.0140.0140.0000.036
education:60.0000.0470.0050.0000.0010.0000.0000.0000.0100.0130.0060.0000.0000.0000.0000.0000.0000.0000.0290.0410.0040.0000.0290.0370.0160.0000.0001.0000.0000.0040.0070.0050.000
marriage:00.0080.0000.0230.0000.0100.0050.0000.0000.0080.0000.0050.0000.0020.0000.0140.0000.0000.0000.0000.0000.0090.0000.0230.0290.0730.0000.0000.0001.0000.0380.0440.0000.011
marriage:10.1000.4780.0540.0570.0530.0590.0550.0500.0310.0240.0230.0240.0260.0240.0000.0090.0000.0070.0050.0080.0300.0010.1540.0610.1120.0000.0140.0040.0381.0000.9750.0950.029
marriage:20.0870.4930.0500.0540.0490.0550.0510.0460.0260.0190.0200.0190.0220.0190.0000.0050.0000.0030.0090.0090.0300.0030.1640.0590.1270.0000.0140.0070.0440.9751.0000.1110.030
marriage:30.0580.0880.0220.0170.0170.0220.0210.0190.0000.0050.0000.0120.0090.0180.0540.0360.0380.0540.0150.0000.0000.0000.0430.0040.0430.0010.0000.0050.0000.0950.1111.0000.007
label0.1570.0480.4220.3400.2940.2780.2690.2490.0310.0310.0000.0190.0170.0220.0270.0130.0240.0220.0350.0280.0390.0080.0510.0360.0320.0240.0360.0000.0110.0290.0300.0071.000

Missing values

2023-02-21T15:00:10.310658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-21T15:00:11.833017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

limit_balagepay_0pay_2pay_3pay_4pay_5pay_6bill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6sex:2education:0education:1education:2education:3education:4education:5education:6marriage:0marriage:1marriage:2marriage:3label
080000.024.00.00.00.00.00.00.075125.077353.078321.073731.039643.039457.03503.05001.02092.01218.01445.0878.01.00.00.01.00.00.00.00.00.00.01.00.00
130000.028.00.00.00.00.00.00.029242.029507.029155.025255.022001.00.05006.01244.0851.0955.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00
2180000.044.00.00.0-1.0-1.0-1.0-1.020916.00.0850.00.06881.010340.00.0850.00.06881.010340.0182.01.00.00.00.00.00.01.00.00.01.00.00.00
360000.025.00.00.00.00.00.00.058839.053235.038533.039639.039619.039140.02018.01900.02000.01500.01900.02000.00.00.01.00.00.00.00.00.00.00.01.00.00
4130000.025.00.00.00.00.00.00.0111587.0112348.0114734.0117823.0120854.0123904.04100.04200.05000.05000.05000.010700.01.00.00.01.00.00.00.00.00.00.01.00.00
520000.032.01.02.00.00.00.00.019844.019238.020205.019588.020037.019880.00.01302.0685.0748.0697.0690.00.00.00.00.01.00.00.00.00.00.01.00.00
6100000.033.0-1.0-1.0-1.0-1.0-1.00.07067.0-418.07064.015229.09689.02669.00.07482.015315.09705.00.04600.01.00.00.01.00.00.00.00.00.01.00.00.00
7210000.031.00.00.00.00.00.00.0205243.0209502.0203831.0178410.0130619.0115700.07736.07100.08300.04800.04396.04200.01.00.00.01.00.00.00.00.00.01.00.00.00
850000.035.00.00.00.00.00.00.013517.014536.015694.016431.017056.017581.01550.01700.01300.0900.0800.0800.01.00.00.01.00.00.00.00.00.00.01.00.00
9360000.043.0-2.0-2.0-2.0-2.0-2.0-2.04435.0799.01071.015584.03195.04261.0805.01071.015604.03195.04269.03525.01.00.00.00.01.00.00.00.00.01.00.00.00
limit_balagepay_0pay_2pay_3pay_4pay_5pay_6bill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6sex:2education:0education:1education:2education:3education:4education:5education:6marriage:0marriage:1marriage:2marriage:3label
29990200000.046.0-2.0-2.0-2.0-2.0-2.0-2.00.00.00.00.00.00.00.00.00.00.00.0600.01.00.00.01.00.00.00.00.00.00.01.00.00
29991360000.027.0-2.0-2.0-2.0-2.0-2.0-2.00.00.07365.00.00.00.00.07365.00.00.00.08665.01.00.01.00.00.00.00.00.00.01.00.00.00
29992360000.039.0-1.0-1.0-1.0-1.0-1.0-1.0396.0396.0396.0396.0396.0846.0396.0396.0396.0396.0846.0396.00.00.01.00.00.00.00.00.00.01.00.00.00
2999330000.023.0-1.0-1.03.02.00.00.06630.03842.03631.01950.01170.01170.03842.00.00.00.00.00.01.00.01.00.00.00.00.00.00.01.00.00.00
2999450000.045.02.00.00.00.00.00.071927.073514.075373.050947.051020.00.03000.03428.02002.01023.00.00.00.00.00.00.00.00.01.00.00.01.00.00.01
29995360000.027.01.0-2.0-1.0-1.0-1.0-1.00.00.0830.00.01271.0179.00.0830.00.01271.0179.01970.01.00.01.00.00.00.00.00.00.00.01.00.00
29996500000.028.02.00.00.02.00.00.098541.0102052.0111690.078070.078376.080912.06000.013151.00.03000.05000.010000.01.00.01.00.00.00.00.00.00.00.01.00.01
2999760000.028.00.00.00.00.00.02.046233.047263.048696.050385.052045.052661.02100.02500.02500.02500.01600.01500.01.00.01.00.00.00.00.00.00.01.00.00.00
2999820000.029.01.0-1.0-1.0-1.0-1.0-1.00.02494.03967.01364.0600.00.02494.03967.01370.0600.00.00.01.00.01.00.00.00.00.00.00.00.01.00.00
29999510000.061.00.00.00.02.00.00.0187070.0181733.0192903.0181801.0178179.0223100.08500.017000.00.06508.050000.07000.01.00.00.00.01.00.00.00.00.01.00.00.00

Duplicate rows

Most frequently occurring

limit_balagepay_0pay_2pay_3pay_4pay_5pay_6bill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6sex:2education:0education:1education:2education:3education:4education:5education:6marriage:0marriage:1marriage:2marriage:3label# duplicates
020000.024.02.02.04.04.04.04.01650.01650.01650.01650.01650.01650.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.012
150000.023.01.0-2.0-2.0-2.0-2.0-2.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.01.00.002
250000.026.01.0-2.0-2.0-2.0-2.0-2.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.01.00.002
380000.025.0-2.0-2.0-2.0-2.0-2.0-2.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.002
480000.031.0-2.0-2.0-2.0-2.0-2.0-2.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.002
580000.042.0-2.0-2.0-2.0-2.0-2.0-2.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.00.002
690000.031.01.0-2.0-2.0-2.0-2.0-2.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.01.00.002
7100000.049.01.0-2.0-2.0-2.0-2.0-2.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.002
8110000.031.01.0-2.0-2.0-2.0-2.0-2.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.01.00.002
9140000.029.01.0-2.0-2.0-2.0-2.0-2.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.002